AbLang2
Score, represent, restore, and review antibody sequences.
Results
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Method & evidence
Choose an AbLang2 analysis to compare sequence plausibility, create fixed-width representations, restore explicit residue masks, or rank substitutions by model probability.
Interpreting results
- Use scores for relative comparison within a consistently prepared candidate set rather than as an experimental pass/fail threshold.
- Higher model scores correspond to lower pseudo-perplexity; lower pseudo-perplexity means the sequence is more expected by the model.
- Each embedding is one sequence-level 480-value representation, not a residue-level embedding or a measured biological property.
- Restoration evaluates all submitted masks simultaneously, preserves every unmasked residue, and reports probabilities over the 20 canonical amino acids.
- Mutation ranks describe preference under the model sequence distribution; they do not establish improved binding, activity, stability, developability, or immunogenicity.
- Heavy-only, light-only, and paired-chain analyses use different context, so compare like with like unless the study design explicitly accounts for that difference.
References
Input requirements & outputs
Input checks
- Variable domains should contain 40–180 unambiguous amino acids per supplied chain
- Embeddings: up to 16 rows and 2,880 total residues
- Restoration: up to 8 rows, 1,440 residues, 16 literal * masks per row, and 64 masks total
- Mutation preferences: one row and up to 360 paired residues; scoring: up to 100 rows and 100,000 residues
Expected results
- Candidate score ranking — Compare confidence or pseudo-log-likelihood and pseudo-perplexity across consistently prepared candidates.
- Sequence representations — Inspect vector summaries in the result view and export complete 480-value embeddings for clustering or downstream models.
- Restored sequences and mask probabilities — Review each restored residue in sequence context with the complete canonical-amino-acid probability distribution.
- Chain-aware mutation preferences — Compare source and proposed probabilities, log-probability ratios, one-based positions, and stable ranks.
